As telecommunications equipment evolves in capability and complexity, and multiple-input and multiple-output (MIMO) systems push the system throughput, it is not going to be too long before we start seeing cognitive radios in the marketplace, as is disclosed in J. Mitola, “Cognitive Radio: An Integrated Agent Architecture for Software Defined Radio,” Ph. D. Thesis, Royal Institute of Technology, Sweden, Spring 2000; and S. Haykin, “Cognitive Radio: Brain-Empowered Wireless Communications,” IEEE J. Select. Areas Commun., vol. 23, no. 2, pp. 201-220, February 2005, the contents all of which are incorporated herein by reference. Cognitive radios will help not just the commercial systems but the military communication systems as well, by doing away with the need for comprehensive frequency planning. In fact, a cognitive radio would be capable of sensing its environment, making decisions on the types of signals present, learning the patterns and choosing the best possible method of transmitting the information. They would be situation aware, and capable of making decisions to ensure error-free and smooth transfer of bits between the users. Cognitive radios will be based on software defined radio (SDR) platforms and will try to understand not only what the users want but also what the surrounding environment can provide. The first step for any cognitive radio will be to understand the surrounding environment and to detect the ambient signals that are present.
The prior art discusses work on signal detection in AWGN using HOS as qualifiers, as is disclosed in B. M. Sadler, G. B. Giannakis, and K. S. Lii, “Estimation and Detection in NonGaussian Noise Using Higher Order Statistics,” IEEE Trans. Signal Processing, vol. 42, no. 10, pp. 2729{2741, October 1994; and G. B. Giannakis and M. Tsatsanis, “A Unifying Maximum-Likelihood View of Cumulant and Polyspectral Measures for Non-Gaussian Signal Classification and Estimation,” IEEE Trans. Inform. Theory, vol. 38, no. 2, pp. 386-406, March 1992, the contents all of which are incorporated herein by reference.
A typical procedure is to collect the signal from the surrounding environment and to identify whether it represents some meaningful information or it is just noise.
A need therefore exists for an improved algorithm system for use in detecting a broad class of signals in Gaussian noise using higher-order statistics for applications such as cognitive radios.